"""
功能：手势操作电脑音量
1、使用OpenCV读取摄像头视频流；
2、识别手掌关键点像素坐标；
3、根据拇指和食指指尖的坐标，利用勾股定理计算距离；
4、将距离等比例转为音量大小，控制电脑音量
"""

import cv2
import mediapipe as mp

# 导入电脑音量控制模块
from ctypes import cast, POINTER
from comtypes import CLSCTX_ALL
from pycaw.pycaw import AudioUtilities, IAudioEndpointVolume

# 导入其他依赖包
import time
import math
import numpy as np


class HandControlVolume:

    def __init__(self):

        # 初始化medialpipe

        self.mp_drawing = mp.solutions.drawing_utils

        self.mp_drawing_styles = mp.solutions.drawing_styles

        self.mp_hands = mp.solutions.hands

        # 获取电脑音量范围

        devices = AudioUtilities.GetSpeakers()

        interface = devices.Activate(

            IAudioEndpointVolume._iid_, CLSCTX_ALL, None)

        self.volume = cast(interface, POINTER(IAudioEndpointVolume))

        self.volume.SetMute(0, None)

        self.volume_range = self.volume.GetVolumeRange()

    # 主函数

    def recognize(self):

        # 计算刷新率

        fpsTime = time.time()

        # OpenCV读取视频流

        cap = cv2.VideoCapture(0)

        # 视频分辨率

        resize_w = 1280

        resize_h = 960

        # 画面显示初始化参数

        rect_height = 0

        rect_percent_text = 0

        with self.mp_hands.Hands(min_detection_confidence=0.7,

                                 min_tracking_confidence=0.5,

                                 max_num_hands=2) as hands:

            while cap.isOpened():

                success, image = cap.read()

                image = cv2.resize(image, (resize_w, resize_h))

                if not success:
                    print("空帧.")

                    continue

                # 提高性能

                image.flags.writeable = False

                # 转为RGB

                image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

                # 镜像

                image = cv2.flip(image, 1)

                # mediapipe模型处理

                results = hands.process(image)

                image.flags.writeable = True

                image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)

                # 判断是否有手掌

                if results.multi_hand_landmarks:

                    # 遍历每个手掌

                    for hand_landmarks in results.multi_hand_landmarks:

                        # 在画面标注手指

                        self.mp_drawing.draw_landmarks(

                            image,

                            hand_landmarks,

                            self.mp_hands.HAND_CONNECTIONS,

                            self.mp_drawing_styles.get_default_hand_landmarks_style(),

                            self.mp_drawing_styles.get_default_hand_connections_style())

                        # 解析手指，存入各个手指坐标

                        landmark_list = []

                        for landmark_id, finger_axis in enumerate(

                                hand_landmarks.landmark):
                            landmark_list.append([

                                landmark_id, finger_axis.x, finger_axis.y,

                                finger_axis.z

                            ])

                        if landmark_list:
                            # 获取大拇指指尖坐标

                            thumb_finger_tip = landmark_list[4]

                            thumb_finger_tip_x = math.ceil(thumb_finger_tip[1] * resize_w)

                            thumb_finger_tip_y = math.ceil(thumb_finger_tip[2] * resize_h)

                            # 获取食指指尖坐标

                            index_finger_tip = landmark_list[8]

                            index_finger_tip_x = math.ceil(index_finger_tip[1] * resize_w)

                            index_finger_tip_y = math.ceil(index_finger_tip[2] * resize_h)

                            # 中间点

                            finger_middle_point = (thumb_finger_tip_x + index_finger_tip_x) // 2, (

                                    thumb_finger_tip_y + index_finger_tip_y) // 2

                            # print(thumb_finger_tip_x)

                            thumb_finger_point = (thumb_finger_tip_x, thumb_finger_tip_y)

                            index_finger_point = (index_finger_tip_x, index_finger_tip_y)

                            # 画指尖2点

                            image = cv2.circle(image, thumb_finger_point, 10, (255, 0, 255), -1)

                            image = cv2.circle(image, index_finger_point, 10, (255, 0, 255), -1)

                            image = cv2.circle(image, finger_middle_point, 10, (255, 0, 255), -1)

                            # 画2点连线

                            image = cv2.line(image, thumb_finger_point, index_finger_point, (255, 0, 255), 5)

                            # 勾股定理计算长度

                            line_len = math.hypot((index_finger_tip_x - thumb_finger_tip_x),

                                                  (index_finger_tip_y - thumb_finger_tip_y))

                            # 获取电脑最大最小音量

                            min_volume = self.volume_range[0]

                            max_volume = self.volume_range[1]

                            # 将指尖长度映射到音量上

                            vol = np.interp(line_len, [50, 300], [min_volume, max_volume])

                            # 将指尖长度映射到矩形显示上

                            rect_height = np.interp(line_len, [50, 300], [0, 200])

                            rect_percent_text = np.interp(line_len, [50, 300], [0, 100])

                            # 设置电脑音量

                            self.volume.SetMasterVolumeLevel(vol, None)

                # 显示矩形

                cv2.putText(image, str(math.ceil(rect_percent_text)) + "%", (10, 350),

                            cv2.FONT_HERSHEY_PLAIN, 3, (255, 0, 0), 3)

                image = cv2.rectangle(image, (30, 100), (70, 300), (255, 0, 0), 3)

                image = cv2.rectangle(image, (30, math.ceil(300 - rect_height)), (70, 300), (255, 0, 0), -1)

                # 显示刷新率FPS

                cTime = time.time()

                fps_text = 1 / (cTime - fpsTime)

                fpsTime = cTime

                cv2.putText(image, "FPS: " + str(int(fps_text)), (10, 70),

                            cv2.FONT_HERSHEY_PLAIN, 3, (255, 0, 0), 3)

                # 显示画面

                cv2.imshow('MediaPipe Hands', image)

                if cv2.waitKey(5) & 0xFF == 27:
                    break

            cap.release()


if __name__ == '__main__':
    control = HandControlVolume()
    control.recognize()
